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Нова лабораторія керування промисловими системами на ФЕЛ kpi пт, 05/08/2026 - 17:48

I have designed and built a test jig that will automatically test a small USB output for bench power supplies adapter called USBpwrME. The USBpwrME allows users to connect USB powered electronics to a power supply during test, evaluation troubleshooting etc. Test jig in action The test jig is built around the PIC18F27K22. This is my goto chip at the moment. It has a lot of configurable peripherals, ADC with really high resolution and a huge amount of memory for being a small MCU. And wide supply voltage range! Test sequence will cover all the functions of the USB adapter with as few operator interactions as possible. One "funny" mistake i made during the design was not noticing that the relays i use has actually polarized coil so the pos/neg has to be connected in correct way to make the relay click. I missed this so i needed to hand modify all three relays. Second mistake i made was actually a bit harder to foresee. One test that is performed is to invert the the input polarity to the USBpwrME to see that the polarity protection works. Well the design mistake was that the GND between the jig and the adapter is connected together thru the GND shield of the USB cables. So when the polarity switches the test jig short-circuits itself and restarts. I solved this by adding in the test sequence when to actually connect the USB cables and performing the polarity test just before. Even my eight year old son can operate it :) :) Quite happy although with the result submitted by /u/KS-Elektronikdesign [link] [comments]

Noise, India’s leading connected lifestyle brand, announces the launch of NoiseFit Halo 3, a bold, design, first round dial smartwatch crafted to seamlessly blend style, productivity and AI-powered utility. Design for those who refuse to compromise, Halo 3 combines the refined aesthetics of a classic dress watch with the intelligence and functionality of a modern smartwatch. It delivers what consumers have long sought: a timeless round-dial design paired with meaningful smart capabilities. Building on the Halo legacy, Halo 3 features a sculpted integrated-strap silhouette, a vibrant 1.43″ AMOLED display with 1000 nits brightness, and Noise AI Pro, a productivity-first AI ecosystem offering voice commands, voice recording and transcription, health insights, and personalised wallpapers. 

With Noise Vault for QR pass access, a customizable Smart Dashboard, one-tap health checks and up to 7 days of battery life, Halo 3 is built for the modern man who wants to make an impression, moving effortlessly from a boardroom meeting to a boarding gate, with a watch that transitions as fluidly as he does.

Noise AI Pro with Smart Productive Dashboard

At the core of Halo 3 lies Noise AI Pro, a productivity-first AI layer built for modern routines. Voice commands enable hands-free actions, morning briefs summarise sleep and activity insights, and AI Transcription transcribes voice notes into clean notes. Super Notifications refine alerts by surfacing contextual updates like OTPs, ride statuses and delivery notifications (Android supported). Complementing this intelligence is a customizable Smart Dashboard that supports up to five widgets,  from music control and AQI to sleep insights and hydration tracking, ensuring the most relevant information is always within reach.

Round-Dial Design with AMOLED Brilliance, built to command attention

NoiseFit Halo 3 features refined curves that flow into an integrated strap design, creating a cohesive, sculpted silhouette. Precision cuts along the dial edge add depth and character, while the 1.43” AMOLED display with 1000 nits brightness delivers striking clarity and effortless visibility across lighting conditions. Available in metal, leather and silicon strap options, Halo 3 adapts seamlessly from boardrooms to social settings, offering long-wear comfort without compromising on presence.

Noise Vault & Seamless Utility, scan and move

Halo 3 introduces Noise Vault, allowing users to store QR codes for flights, concerts, movies and more directly on the watch. Acting as a digital passbook, it enables seamless, hands-free scanning at entry points and boarding gates, reducing dependence on the phone during high-movement moments.

Health Insights & Week-Long Battery, built for uninterrupted days

The smartwatch supports one-tap heart rate, stress and SpO₂ monitoring alongside continuous tracking throughout the day. Backed by up to 7 days of battery life, Halo 3 ensures users stay informed and connected without frequent charging interruptions.

Price and Availability

Available in four elegant colours with strap options – Metal (Black) , Leather (Brown, Blue) & Silicon (Black),  the NoiseFit Halo 3 is live on sale, at an introductory price of 5,499 on gonoise.com, Amazon and Flipkart. 

Product Specifications
NoiseFit Halo 3

Specification	Details
Display	1.43″ AMOLED, 1000 nits
Strap options	Metal (Black), Leather (Brown, Blue), Silicon (Black)
Core AI	Noise AI Pro: Voice commands, Morning briefs, AI Transcription, Super Notifications (Android-only advanced notifications)
Health	One-tap Heart Rate, Stress, SpO₂; continuous tracking
Compatibility	Android & iOS
Battery	Up to 7 days

 

About Noise

Noise is India’s leading smartwatch and connected lifestyle brand. The brand prioritises consumer centricity, design innovation, and product excellence to constantly reinvent and introduce future-forward innovations in audio, wearables, and the connected lifestyle ecosystem. As a homegrown brand, it is committed to creating an experience-led ecosystem through futuristic yet meaningful technology. With patents and a strong R&D focus, their innovation arm, Noise Labs, boasts many industry-first breakthroughs and houses some stellar technologies across categories. 

Noise is leading the charge to foster the growth of the industry and the nation’s vision by boosting the manufacturing efforts under the Make in India initiative, fostering a strong community of people who want to connect on health, lifestyle, and fitness on the NoiseFit App, while helping businesses ensure their employee wellbeing through the Corporate Wellness Program.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The post Next-Gen Upgrade to the Halo Series, NoiseFit Halo 3 brings Presence-Led Design and AI to the Wrist appeared first on ELE Times.

Renesas Electronics Corporation is a premier supplier of semiconductor solutions. Today, it announces that a subsidiary of Renesas has completed the acquisition of Irida Labs, a Greece-based company specialising in embedded software for AI-powered visual perception systems. The acquisition strengthens Renesas edge AI embedded processing offerings, a key secular growth area for Renesas. It also enables system-level solutions that integrate physical AI vision systems across industrial, robotics, smart city, IoT, agriculture and healthcare markets. As a part of Renesas’ digitalisation strategy, Irida Labs software and tools will be integrated into Renesas 365, a newly released platform that unifies electronics system development from discovery to development and lifecycle management.

While the demand for intelligent systems at the edge continues to soar across industries, developers must often overcome the growing complexity of AI system development. This includes the integration of power-constraint embedded processors and software, training, deploying AI models and addressing latency and security risks associated with data transmission. Vision AI software plays a critical role in interpreting and processing visual data from cameras and sensors widely used in industrial inspection, robotics guidance, in-cabin automotive sensing, traffic and infrastructure monitoring, smart retail analytics and safety and security systems.

The addition of Irida Labs to Renesas’ product portfolio addresses these emerging challenges. By combining Renesas’ AI-enabled RA microcontrollers (MCU) AND RZ microprocessors (MPU) with Irida Labs comprehensive tool suite and lightweight Vision AI software, Renesas can now delebier high performance, power-efficient edge AI solutions that are ready for deployment. Together, these capabilities reinforce Renesas’ progress towards fully integrated Vision AI system solutions.

Vassilis Tsagaris, CEO & Co-Founder of Irida Labs, added, “The joining of Irida Labs into Renesas marks an important milestone in our edge vision AI journey. By combining Irida Labs’ edge Vision AI expertise and our PerCV.ai software with Renesas hardware and global ecosystem, we open up exciting new opportunities to deliver meaningful impact on edge AI worldwide. I am proud of what the team has built, and genuinely excited to take it forward together with Renesas, turning our shared vision into reality.”

Before the acquisition, Renesas and Irida Labs collaborated as partners to develop solutions combining Irida Labs’ PerCV.ai software with Renesas’ RA and RZ devices. Bringing these capabilities in-house enables Renesas to deliver more tightly integrated solutions quickly. Renesas also plans to integrate Irida Labs software and tools into its newly introduced intelligent, open cloud-based development platform, Renesas 365.

The post Renesas Completes Acquisition of Irida Labs to Expand Vision AI Software Capabilities and Accelerates System-Level Vision Solutions appeared first on ELE Times.

День пам’яті та перемоги над нацизмом у Другій світовій війні 1939–1945 років KPI4U-2 пт, 05/08/2026 - 11:56

Infineon Technologies AG of Munich, Germany says that the Full Commission of the US International Trade Commission (ITC) has affirmed the ITC’s initial determination from December 2025 that China-based Innoscience (Suzhou) Technology Holding Co Ltd, which manufactures GaN-on-silicon power chips on 8” silicon wafers, infringed an Infineon patent concerning gallium nitride (GaN) technology and ordered import and sales bans against Innoscience. The Commission’s final decision and the bans are subject to a 60-day review period of the US President...

Rhode & Schwartz and Greenerwave conduct a joint measurement trial that demonstrates a near-field system. It can record the full radiation pattern of a 50 cm Ku band electronically steerable array for a SATCOM antenna in just half an hour. The result matches simulation models within a decibel, that make this approach rapid and precise. For manufacturers of SATCOM systems facing large chamber constraints, it offers a clear path towards quick and cost-effective testing.

Electronically Steerable Array (ESA) antennas are a key component in modern SATCOM systems. Accurate knowledge of their radiation pattern is required for reliable operation in LEO, MEO, and GEO orbits. However, conventional far-field testing demands chambers that are often larger for Ku or Ka band antennas, especially when the aperture of the Antenna Under Test (AUT) reaches half a meter or more. Compact Antenna Test Range (CATR) is relatively large for AUTs and are time consumiong dual-axis positioning of AUT to map the radiation pattern.

Greenerwave’s innovative SATCOM user terminals are based on Reconfigurable Intelligent Surface (RIS), allowing the company to design electronically steerable antennas that deliver high-performance connectivity while reducing energy consumption and reliance on semiconductors compared with conventional solutions.

For the joint measurement campaign, T&M expert Rhode & Schwarz provided its R&STS8991 over-the-air and antenna measurement system, equipped with a conical cut positioner, and its R&SZNA vector network analyser. Together, they evaluated Greenerwave’s passive single-aperture ESA that uses RIS technology for beamforming. The Antenna Under Test (AUT) features a 50x 50cm aperture and is designed for low power consumption and easy integration.

The measurement covered an extended upper hemisphere down to a polar angle of 120 degrees, using a one-degree step size. Ten Ku band frequencies were recorded in a total of 32 minutes due to the system’s hardware trigger function. Data was processed using the R&SAMS32 antenna measurement software, which applied the FIAFTA near-field to far-field transformation.

Comparison with the original simulation based on a numerical twin model and with results from Greenerwave’s CATR setup showed peak gain or directivity variations, validating the accuracy of the near-field solution. The trial shows that even large SATCOM antennas can be characterised quickly and accurately, providing a practical alternative to large-sized far-field CATRs. This system can be used by other SATCOM makers testing broadband, research lab environment, IoT for applications requiring flexible beam control and high data rates.

The post Rohde & Schwarz and Greenerwave Achieve an Efficient ESA Antenna Charecterisation Using Near-Field Technology appeared first on ELE Times.

Deposition equipment maker Aixtron SE of Herzogenrath, near Aachen, Germany has supplied Renesas Electronics Corp of Tokyo, Japan with multiple Planetary G5+C systems to expand its gallium nitride (GaN) production in high-volume manufacturing (HVM) environments. The collaboration helps to strengthen Renesas’ GaN production capabilities in response to surging demand across critical power electronics applications...

The current robotics narrative is heavily weighted toward artificial intelligence (AI). The prevailing assumption is that more parameters, larger models, and better reinforcement learning pipelines will eventually grant machines human like dexterity. This belief has shaped research agendas, funding priorities, and public expectations.

However, for engineers designing hardware that must survive millions of high-velocity cycles at companies like Amazon Robotics, a different truth is apparent. In the lab, the focus is on the brain, but on the production floor, robots fail for mechanical reasons far more often than algorithmic ones.

In high duty cycle environments, the primary drivers of unplanned downtime are wear, compliance, thermal drift, misalignment, and mechanical fatigue. These are not failures of perception or planning. No amount of neural network tuning can compensate for a linkage that deflects under load or an end effector that cannot maintain repeatability. As the industry continues to chase AI-centric solutions, it risks overlooking the fundamental engineering disciplines that determine whether a robot succeeds in the physical world.

The robotics community is at a crossroads. The last decade has delivered extraordinary advances in machine learning, but the physical reliability of robotic systems has not kept pace. The result is a widening gap between what robots can demonstrate in controlled environments and what they can sustain in real production settings.

Closing this gap requires a shift in mindset. The next leap in robotics will not come from larger models or more training data. It will come from better mechanisms, better actuation, and better physical architectures.

The reliability gap

The industry has spent a decade optimizing the brain while neglecting the body. This imbalance has created what can be described as the reliability gap. As a technical judge for MassChallenge and for university capstone programs at Worcester Polytechnic Institute and Boston University, I have observed a recurring pattern.

Startups and student teams often present systems that segment objects perfectly in simulation, classify scenes with remarkable accuracy, and demonstrate impressive reinforcement learning policies. Yet when these systems are deployed in the physical world, they fail after only a few hours of operation.

The reason is straightforward. AI amplifies a robot’s capability, but the mechanism defines the physical boundary. If a kinematic chain introduces unpredictable hysteresis, software cannot compensate its way to a reliable solution. If a transmission loses stiffness under load, no amount of perception accuracy will restore positional integrity. If an end effector cannot generate stable contact forces, even the most advanced grasping model will fail.

The robotics industry must acknowledge a practical reality. Software and AI are essential, but they cannot overcome fundamental mechanical limitations. The most successful robotic systems in history have not been those with the most advanced algorithms, but those with the most deterministic mechanical behavior. Reliability is not an emergent property of software. It’s engineered into the physical system from the beginning.

Determinism and the voyager philosophy

True industrial progress requires a return to mechanical rigor, specifically a focus on what can be called deterministic mechatronics. This philosophy suggests that the most successful robotic systems are those engineered for passive stability, predictable behavior, and graceful failure. A useful analogy comes from deep space engineering.

Voyager 1, launched nearly half a century ago, remains operational in one of the harshest environments imaginable. NASA has occasionally uploaded new command sequences, performed resets, and adjusted subsystems to extend its life. These interventions succeed because the underlying mechanical and electrical systems were engineered for extreme reliability. The spacecraft’s longevity is not the result of software alone or hardware alone, but the synergy between robust physical design and intelligent control.

Industrial robotics should adopt this same mindset. The next leap in automation will come from kinematic architectures that reduce inertia, precision transmissions that maintain sub-millimeter accuracy under load, and actuation strategies that prioritize physical determinism. The goal is not to diminish the role of AI, but to ensure that AI is built on a stable mechanical foundation.

A deterministic mechanism reduces the burden on perception and control. It narrows the solution space. It transforms a difficult control problem into a manageable one. When the physical system behaves predictably, the software becomes simpler, more robust, and more efficient.

Case study: The apparel challenge

The manipulation of non-rigid materials, such as apparel, provides a clear example of this principle. Handling folded fabric is traditionally viewed as an AI problem. The common assumption is that complex pose estimation, dense depth reconstruction, and advanced vision models are required to manage the noise introduced by folds and wrinkles.

However, breakthroughs in this field, including those protected under U.S. Patents 11268223 and 11939714, demonstrate that the solution is primarily mechanical. By designing a compliant yet deterministic gripping architecture, the physics of the material can be used to the machine’s advantage.

When the kinematic chain is engineered to minimize shear forces, the physical interaction becomes predictable. When the mechanism constrains the degrees of freedom in a way that aligns with the material’s natural behavior, the need for complex perception is reduced.

In these systems, AI still plays a meaningful role. It identifies features, guides sequencing, and handles variability. But it succeeds because the underlying mechanism provides a stable substrate. The machine does the heavy lifting so the software can remain efficient. This balanced approach is what the industry needs. Instead of using software to compensate for mechanical unpredictability, the mechanism is engineered to reduce the burden on software.

This approach scales. It is robust. It is repeatable. And it is the foundation on which industrial grade automation must be built.

A new hierarchy of design

To unlock the next stage of automation, the engineering community must rebalance its priorities. The hierarchy of design must shift.

First, the industry must invest in mechanism research and development with the same intensity it brings to AI. For every dollar spent on perception, equal resources should be allocated to transmissions, linkages, and end effectors. Mechanisms are not a solved problem. They are the frontier that will determine the next decade of progress.

Second, the industry must build reliability-first architectures. Robots should be engineered with the longevity of aerospace systems, not the lifecycle of consumer electronics. This requires a shift in mindset. Reliability is not a feature. It’s a design philosophy.

Third, the industry must foster a new breed of roboticists. The next generation of engineers must be equally proficient in kinematics and PyTorch, equally comfortable with finite element analysis and neural network training and equally invested in mechanical determinism and algorithmic efficiency. The future belongs to engineers who can bridge the physical and digital domains.

Finally, the industry must resist the temptation to chase demos. The goal is not to produce systems that perform well in controlled environments, but systems that operate reliably in the real world. The measure of success is not a viral video, but a robot that performs millions of cycles without failure.

The next decade of robotics

Artificial intelligence is an extraordinary amplifier, but it’s not the foundation of robotics. Intelligence can only be as effective as the physical vessel through which it acts. The next decade of robotics will be defined by the engineers who recognize that mechanisms, transmissions, and physical architectures are not secondary considerations. They are the core of the system.

The future of robotics does not belong to the AI-first approach or the mechanism-first approach. It belongs to the integration of both into a single, reliable, and deterministic system. When the body and the brain evolve together, automation will finally achieve the scale, reliability, and capability that the industry has been pursuing for years.

This is the mechanism-centric future of robotics. And it’s long overdue.

Santosh Yadav is senior mechanical engineer and robotics researcher at ASME MBE Standards Committee.

Special Section: Smart Factory

• Rethinking machine vision in industrial automation
• Smart factory: The rise of PoE in industrial environments
• Precision lasers boost safety and efficiency in smart factories
• Tale of 3 sensors operating in smart factory environments
• From edge AI to physical AI in smart factories: A shift in how machines perceive and act

The post Robots: Why AI alone will not deliver the next leap in automation appeared first on EDN.

📰 Газета "Київський політехнік" № 17-18 за 2026 (.pdf) Інформація КП пт, 05/08/2026 - 10:00

Of course, the process was not completely smooth. I wanted to add reverse polarity protection to V1, which the prototype did not have. In the first design, I built and tested a reverse polarity protection circuit with a single P-Channel MOSFET. However, I had missed one scenario: although a single P-Channel MOSFET can be enough in some cases, it could not block reverse current coming from inside the device. Even when the IRFP260N MOSFETs were off, reverse current could pass through the body diodes and put the connected power supply into a short-circuit condition. To solve this problem, I reworked the PCB to convert the power input block to a back-to-back P-Channel MOSFET structure. I used the banana sockets on the front panel as the protected input, designed to support an 8-30V range. The XT60 connector on the right works as the unprotected input and supports a 0-30V input range. After the rework, the protected power input caused significant heating at 8V and below because it left the protection MOSFETs partially on. For the next PCB revision, I plan to redesign the power input block using an ideal diode controller and two low-RDS(on) N-Channel MOSFETs in a back-to-back structure. Also, because of the two P-Channel back-to-back MOSFETs, the protection MOSFETs heated to unsafe levels at my target 200W test power. For safe operation, I limited the device to 150W. The device can support voltage and current values up to 30V and 10A within this limit. On the software side, with AI assistance, I developed control, protection and monitoring functions such as toggling load draw with the RST button, overcurrent warning, reverse polarity notification, temperature tracking and fan control. For the V2 revision, I aim to improve the device with more functional features and design a structure with higher power capacity. Overall, this project was a very educational and experience-building work for me in power electronics, measurement, PCB design, mechanical design, rework and fault analysis. https://omerikinci.github.io/projects/electronic-dummy-load.html submitted by /u/Aggravating-Safe5352 [link] [comments]

Kept redoing the same impedance calculations during SI work, so I built this into a proper tool. It covers: Microstrip, Stripline, Coaxial, CPW, GCPW, Differential Pair — gives you Z₀, εeff, propagation delay, loss, VSWR, Smith Chart, dispersion analysis, and parametric heatmaps. Runs in browser, no login, no account. Link: tools.vyomex.in/Impedance_calculator Happy to hear if anything is off or if there are topologies you'd want added. submitted by /u/Existing-Milk3177 [link] [comments]

submitted by /u/1Davide [link] [comments]

The Canadian government is to spin off the Canadian Photonics Fabrication Centre (CPFC) — the nation’s only end-to-end pure-play III-V compound semiconductor wafer manufacturing facility — into a commercial entity...

The evolution of automotive safety is moving from the exterior to the interior, opening a new frontier: in-cabin sensing. Its emergence marks a shift from passive vehicle shells to active systems capable of detecting and safeguarding occupants. However, implementing radar-based in-cabin sensing presents multifaceted engineering challenges, including privacy considerations, real-time data processing, and functional safety, all under strict regulatory umbrella.

Radar has become the preferred modality for in-cabin applications, offering privacy by design, effectiveness through interior materials, and immunity to lighting conditions. Crucially, it detects micro-motions such as breathing and heartbeat.

Why in-cabin sensing Is becoming mandatory

In-cabin sensing includes systems that monitor driver behavior, track occupant presence, detect vital signs, and recognize gestures within the vehicle. With the push for in-cabin sensing in response to global demand for higher safety standards, in-cabin sensing is moving from a “nice-to-have” to a “must-have” feature set.

Figure 1 In-cabin sensing is increasingly becoming a must-have feature in modern vehicles. Source: Cadence Design Systems

Tragic incidents involving children left in hot cars and drowsy driving have prompted regulators and safety organizations to act, making in-cabin sensing essential for top safety ratings.

Regulatory bodies are shifting focus from external crash prevention to interior safety measures. Programs like Euro NCAP’s Child Presence Detection (CPD), effective in 2025, and the U.S. Hot Cars Act highlight the importance of interior monitoring to prevent child fatalities and assess driver alertness. While traditional camera systems face privacy and lighting challenges, radar technology, especially 60 GHz frequency-modulated continuous wave (FMCW) radar, offers a superior, privacy-preserving solution for next-generation intelligent cockpits.

Why radar is emerging as a preferred modality

Radar technology offers a unique set of capabilities that make it the optimal choice for the complex environment of a vehicle cabin. Unlike cameras, which can be obstructed by poor lighting or raise privacy concerns, radar provides robust, non-intrusive sensing and offers many benefits.

Privacy by design

In an era where data privacy is paramount, radar offers a distinct advantage. It does not capture detailed visual images of faces or bodies. Instead, it detects presence and movement through point clouds. This allows the system to monitor occupants effectively without recording sensitive personal visual data, making it far more acceptable to privacy-conscious consumers.

Seeing the unseen (non-line-of-sight)

One of the most profound advantages of radar is its ability to penetrate materials. A camera cannot see a child covered by a blanket or sleeping in a rear-facing car seat obstructed by the driver’s seat. Radar, however, can detect the micro-movements of breathing or a heartbeat through clothing, blankets, and even seat materials (excluding steel). This non-line-of-sight (NLOS) capability is crucial for reliable CPD.

Environmental robustness

Radar is immune to lighting conditions. It functions just as effectively in pitch-black darkness as it does in blinding sunlight, ensuring continuous protection day or night. Furthermore, its performance remains robust despite temperature fluctuations, humidity, or vibrations—common factors in the automotive environment.

Why 60-GHz FMCW radar specifically?

As OEMs and Tier 1 manufacturers evaluate their platform choices, the FMCW-versus-ultra-wideband (UWB) debate often arises. While UWB has had success in consumer electronics and certain automotive access systems, FMCW radar aligns more naturally with the requirements of high-volume automotive in-cabin sensing deployments.

FMCW offers a lower cost structure, simpler integration path, and superior feature scalability. It supports multi-use sensing—from occupant monitoring and CPD to vital signs and gesture recognition—all within a unified signal-processing pipeline.

FMCW also avoids security challenges such as relay or “man-in-the-middle” vulnerabilities sometimes associated with UWB applications. Taken together, these factors make FMCW at 60 GHz the “sweet spot” for OEMs targeting a multi-model rollout between 2026 and 2030.

Challenges in engineering the intelligent cabin

Implementing radar-based in-cabin sensing is not without its challenges. It represents a multifaceted engineering hurdle that requires the convergence of precision sensors, high-speed signal processing, and functional safety compliance.

The processing challenge

Detecting the subtle rise and fall of a sleeping infant’s chest amidst the noise of a moving vehicle requires immense computational precision. The radar processing pipeline involves complex stages, including the Range FFT (Fast Fourier Transform), the Doppler FFT, and sophisticated clutter-removal algorithms.

Statistics show 99.9% accuracy in CPD using radar. To achieve this high accuracy, engineers must employ advanced digital signal processing (DSP) technologies. Solutions like the Tensilica Vision 110 DSP are designed specifically for these high-performance, low-power requirements.

Figure 2 Here is a radar processing pipeline for a child presence detection use case. Source: Cadence Design Systems

By offloading complex mathematical operations such as 8-bit and 16-bit MACs to a dedicated DSP, automotive designers can achieve the required frame rates (around 50 FPS) while adhering to strict power and thermal constraints.

Integrating AI and machine learning

The future of in-cabin sensing lies in the fusion of traditional signal processing with machine learning (ML). While traditional algorithms excel at determining distance and speed, ML is essential for classification. Is the object a bag of groceries or a child? Is the driver blinking due to fatigue or just natural movement? Object segmentation is performed by running AI models on a radar dataset.

Advanced radar architectures now support AI-driven classification, allowing the system to learn and adapt. This capability enables features like gesture recognition for touchless control of infotainment systems, adding a layer of comfort and convenience alongside safety.

Applications beyond safety: Comfort and autonomy

While safety mandates are the primary driver, the potential of radar-based in-cabin sensing extends well beyond user experience and autonomous operation.

Health and wellbeing

The sensitivity of 60-GHz radar enables vital sign monitoring. Systems can continuously track heart and breathing rates without physical contact.

Figure 3 This radar processing pipeline serves vital signs monitoring (HR/BR). Source: Cadence Design Systems

In the event of a medical emergency, the vehicle could detect the driver’s distress and autonomously pull over or alert emergency services.

Enhancing autonomy

As we progress toward L3 and L4 autonomy, the vehicle needs to know not just where it is, but also how its occupants are doing. In a handover scenario where the car needs the driver to take control, the in-cabin sensing system must verify that the driver is alert, present, and ready. Radar provides this verification reliably, acting as a core intelligence layer that builds trust in machine-driven environments.

Operational efficiency

For emerging mobility models like robotaxis, radar offers practical benefits. It can detect the number of passengers for billing purposes, ensure no objects are left behind, and even automatically manage trunk operation.

The silicon imperative: Efficient DSPs and AI at the edge

In-cabin radar workloads demand a unique blend of high-throughput DSP operations and compact neural-inference capabilities. Traditional MCUs lack the parallelism required for FFT-heavy pipelines, while dedicated NPUs often exceed cost and power envelopes for cabin modules. A new category of radar-optimized DSPs has emerged as the right balance—programmable, efficient, and capable of supporting both classical signal processing and radar-trained neural networks.

These processors must deliver high MAC throughput, robust SIMD capabilities, and efficient memory architecture while operating within tight thermal constraints. Their flexibility enables quick algorithmic iteration, which is essential in a domain where radar datasets continue to expand across body sizes, seating layouts, and vehicle architectures.

The road ahead

As vehicles advance toward autonomous operation, in-cabin sensing will become a core intelligence layer that predicts occupant needs, safeguards their well-being, and builds trust in machine-driven environments. The integration of radar into the vehicle cabin is redefining what it means to be safe on the road.

For automotive OEMs and Tier 1 suppliers, mastering scalable, radar-based sensing architecture is no longer optional, but is a determinant of future leadership. By leveraging powerful DSP platforms and embracing the unique capabilities of FMCW radar, engineers are not just meeting regulations; they are designing a safer, more intuitive driving experience.

The guardians are no longer just on the bumper; they are inside, ensuring that every journey ends as safely as it began.

Amit Kumar is director of Automotive Product Management and Marketing for Tensilica DSPs at Cadence. He has more than 20 years of design experience in the semiconductor and IP segments. Amit has held product marketing, application engineering, business development, and key strategic management roles with a specialization in automotive ADAS/AD and robotics applications.

Related Content

• Automotive: The latest on in-cabin sensing designs
• Partnering to Advance Automotive In-Cabin Sensing Tech
• In-Cabin Monitoring: Time-of-Flight and Radar Take the Wheel
• How In-Cabin Monitoring Solutions Contribute to Overall Vehicle Safety
• Advancements in radar technology and the evolution of in-cabin sensing

The post The guardians inside: How radar is redefining in-cabin sensing appeared first on EDN.

VSDSquadron FPGA Trainer Kit for High School Chip Design is now ready to ship — a complete hands-on platform to learn RISC-V, FPGA, and real chip design from school level. submitted by /u/kunalg123 [link] [comments]

SmartClamp DrMOS power devices from AOS are designed for the demanding power requirements of AI servers and high-end GPUs. Each device is a synchronous buck power stage with two asymmetrically optimized high-side and low-side MOSFETs and an integrated driver. They provide precise 100-A positive and 50-A negative current limiting during high di/dt transients. The flagship AOZ53228QI extends protection to multiphase voltage regulators, helping prevent failures during frequent high peak-current events.

In AI applications, fast load transients can drive current beyond the limits of standard inductors and power stages. Conventional overcurrent protection schemes may introduce response delays that allow short current overshoot events, which can stress the high-side MOSFET, particularly under inductor saturation conditions.

The SmartClamp family mitigates this risk by implementing current limiting directly within the power stage rather than relying solely on the controller, improving response to load transients that occur in tens of nanoseconds. An internal ramp-based sensing method continuously monitors inductor current in real time, enabling cycle-by-cycle current clamping instead of reacting after fault conditions develop. Cycle-by-cycle control reduces the likelihood of inductor saturation and MOSFET overstress during AI-style burst loads.

SmartClamp devices, including the AOZ53228QI, AOZ53262QI, and AOZ53263QI, are available in production quantities with a 12-week lead time. The AOZ53228QI is priced at $1.40 each in lots of 1000 units.

Alpha & Omega Semiconductor 

The post Protected DrMOS ICs enable fast AI current limiting appeared first on EDN.

SiTime’s Elite 2 Super-TCXO family of oscillators delivers sub-nanosecond synchronization, increasing GPU utilization in AI clusters. By minimizing timing errors between GPUs, the devices boost throughput and performance per watt.

“Industry reports show GPU utilization in AI clusters can be as low as 20 to 40 percent—a large and largely hidden tax on AI infrastructure,” said Piyush Sevalia, chief business officer at SiTime. “AI workloads are distributed across GPUs in tightly orchestrated time slots. Even small timing errors force wait cycles to avoid data corruption, and in extreme cases can trigger GPU timeouts and system restarts. Poor synchronization directly caps GPU utilization.”

Emerging AI cluster requirements call for reducing timing errors to 10 ns, down from 1 µs today. The Elite 2 Super-TCXO achieves 1-ns synchronization accuracy—exceeding this target—with frequency slope as low as ±2 ppb/°C.

The series comprises four variants: SiT5234 and SiT5434, operating from 1 MHz to 60 MHz, and SiT5235 and SiT5435, operating from 60 MHz to 105 MHz. The SiT5234 and SiT5235 offer Allan Deviation (ADEV) of 1E-11, while the SiT5434 and SiT5435 achieve 6E-12. All oscillators are available in 3.2×2.5-mm plastic and 5.0×3.2-mm ceramic packages.

Elite 2 Super-TCXOs are sampling now, with commercial production expected in Q3 2026.

SiTime

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TVS diodes in the TPSMC, TPSMD, and TP5.0SMDJ series from Littelfuse provide standoff voltage ratings of up to 400 V in a single device. Compared to low- and mid-voltage TVS diodes that require multiple devices in series for adequate protection, this single-device approach reduces BOM costs and component count.

The TPSMC, TPSMD, and TP5.0SMDJ series deliver peak pulse power ratings of 1.5 kW, 3.0 kW, and 5.0 kW (10/1000 µs), respectively, with peak surge currents up to 300 A. Designed for automotive power electronics, the devices protect GaN/SiC MOSFETs and IGBTs in battery disconnect units, high-voltage HVAC systems, and PTC heaters from severe transients such as load dumps and other high-energy events.

These devices combine fast response times (typically <1 ps) for effective transient clamping with IEC-61000-4-2 ESD compliance up to 30 kV for robust system-level protection. AEC-Q101 qualification and PPAP capability support automotive reliability requirements, while the SMC (DO-214AB) surface-mount package minimizes PCB footprint and simplifies layout.

The TPSMC, TPSMD, and TP5.0SMDJ series are available in tape-and-reel format in quantities of 3000. Samples can be requested through authorized Littelfuse distributors worldwide.

Littelfuse

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R&S has extended its BBA300 family of broadband amplifiers with single-band models delivering 500 W and 1000 W P1dB RF output power. The BBA300-DE500 and BBA300-DE1000 cover 1 GHz to 6 GHz without band switching, improving efficiency in automated test environments. Optional BBA-PK1 software for the 500-W model enables bias point adjustment to optimize either linearity for complex signals or pulse fidelity, while providing a tradeoff between output power and mismatch tolerance.

Well-suited for automotive, aerospace, and defense applications, the solid-state amplifiers offer high availability and robust operation under mismatch conditions. They generate high field strengths for component and full-vehicle testing, as well as high-intensity radiated field (HIRF) testing. The amplifiers support a wide range of modulation types, from standard amplitude and pulse modulation to complex OFDM signals.

To achieve high power density, the compact modular amplifiers integrate into 30U racks preconfigured for direct horn antenna mounting. To reduce RF losses at high frequencies, the RF output is positioned centrally within the rack, minimizing cable length to the antenna and improving overall link budget.

Learn more about the BBA-300 family of broadband amplifiers here.

Rohde & Schwarz 

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